Millimeter-wave (mmWave) massive multi-user multiple input multiple output (MU-MIMO) systems employ hybrid analog-digital precoding/combining design, to reduce the number of radio-frequency (RF) chains without large sum-rate performance loss. Committee machines combine several expert algorithms to yield better reliable estimates to those of its constituent experts. However, several committee machines approach mainly rely on the ‘quality’ of the joint support-set of expert algorithms combine by committee machine. In this paper, we present hybrid analog-digital precoder and combiner designs for mmWave massive MU-MIMO systems by exploiting the intersection of the estimated support-sets, namely, common support-set of ‘expert’ algorithms in a committee machine methodology. With S denoting the sparsity level, the proposed algorithms perform two essential tasks to identify in which subspace, generated by not more than S columns of the matrix of array response vectors, the optimal hybrid analog-digital precoders or combiners lie: Firstly, the selection of the committee machine common support set, which accepts the section where both the ‘experts’ agree. Secondly, if some column indices of the optimal hybrid analog-digital precoders or combiners do not lie in the current common support-set for the correct spanning space, −the algorithm searches over the matrix of array response vectors and selects column indices that exhibit the highest correlation with the signal residual, to acquire the remaining support. We derive the theoretical analysis for performance enhancement of the proposed algorithm. Simulation results demonstrate that the proposed design allows mmWave massive MU-MIMO systems to approach their unconstrained performance limits, and exhibits substantial sum-rate performance improvement over existing regularized channel diagonalization based schemes and the beam steering solution.